Genomic insights into Mycobacterium tuberculosis and its interaction with the microbiota

Tuberculosis (TB) is the leading cause of death from a bacterial infection in humans. Despite its impact throughout history on humans across the globe, it remains challenging to diagnose and treat. This work used molecular biology and next generation sequencing to explore these issues. First, in a study to identify potential biomarkers of TB infection, the interaction between Mycobacterium tuberculosis (the causative agent of TB), the mouse immune system, and the murine gut microbiota was examined. The murine gut microbiota was observed to respond specifically to M. tuberculosis infection in several host genotypes, and these changes were most likely mediated by the adaptive immune system. Together, these data confirm that the response of the gut microbiota can be further explored for TB diagnostics. A second study was aimed at understanding the genetic mechanisms of resistance to a novel anti-mycobacterial compound. Resistance was mediated through loss of function of Rv2887, a previously unannotated gene that was found to be a multiple antibiotic resistance repressor (MarR) transcriptional regulator. Analysis of the function of Rv2887 led to the identification of a gene regulation mechanism that could be a potential new drug target. Finally, in a third study the genetic basis of geographic restriction of M. africanum, a mycobacterial species that causes similar disease to human TB but is usually only found in West Africa, was elucidated. Despite conventional dogma, analysis of M. africanum using new bioinformatics tools revealed that it is not a separate species from M. tuberculosis. Furthermore, M. africanum is unimpaired in transmission or virulence compared to M. tuberculosis, thus suggesting that the geographic restriction may be due to host factors. Taken together, this work explores the host-pathogen interactions and genetics of mycobacteria and provides novel insights into how these bacteria cause TB

SmartSet Virtual Studio Solution: Validation Phase Test Results

The vision in the SmartSet project is to develop a low cost virtual studio solution that, despite being ten times less than the cost of comparable solutions on the market, will have the same quality of high cost solutions currently used by larger broadcast media companies, but with a simple and limited functionality. The project will increase the competitiveness of the European creative industries, particularly in the broadcast media sector. The SmartSet project objectives include mapping and prioritising the user requirements for the virtual studio solution to be developed. This report is based on the user consultation process with the end users and stakeholders of the SmartSet project to determine the functionality requirements for product development and integration. The research set out to detail a range of user requirements which will feed into the virtual studio specification

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases

Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2–69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation.Code available at:https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171

Mutation of Rv2887, a marR-Like gene, confers mycobacterium tuberculosis resistance to an imidazopyridine-based agent

Drug resistance is a major problem in Mycobacterium tuberculosis control, and it is critical to identify novel drug targets and new antimycobacterial compounds. We have previously identified an imidazo[1,2-a] pyridine-4-carbonitrile-based agent, MP-III-71, with strong activity against M. tuberculosis. In this study, we evaluated mechanisms of resistance to MP-III-71. We derived three independent M. tuberculosis mutants resistant to MP-III-71 and conducted whole-genome sequencing of these mutants. Loss-of-function mutations in Rv2887 were common to all three MP-III-71-resistant mutants, and we confirmed the role of Rv2887 as a gene required for MP-III-71 susceptibility using complementation. The Rv2887 protein was previously unannotated, but domain and homology analyses suggested it to be a transcriptional regulator in the MarR (multiple antibiotic resistance repressor) family, a group of proteins first identified in Escherichia coli to negatively regulate efflux pumps and other mechanisms of multidrug resistance. We found that two efflux pump inhibitors, verapamil and chlorpromazine, potentiate the action of MP-III-71 and that mutation of Rv2887 abrogates their activity. We also used transcriptome sequencing (RNA-seq) to identify genes which are differentially expressed in the presence and absence of a functional Rv2887 protein. We found that genes involved in benzoquinone and menaquinone biosynthesis were repressed by functional Rv2887. Thus, inactivating mutations of Rv2887, encoding a putative MarR-like transcriptional regulator, confer resistance to MP-III-71, an effective antimycobacterial compound that shows no cross-resistance to existing antituberculosis drugs. The mechanism of resistance of M. tuberculosis Rv2887 mutants may involve efflux pump upregulation and also drug methylation

Aerosol <i>Mycobacterium tuberculosis</i> Infection Causes Rapid Loss of Diversity in Gut Microbiota

<div><p><i>Mycobacterium tuberculosis</i> is an important human pathogen, and yet diagnosis remains challenging. Little research has focused on the impact of <i>M. tuberculosis</i> on the gut microbiota, despite the significant immunological and homeostatic functions of the gastrointestinal tract. To determine the effect of <i>M. tuberculosis</i> infection on the gut microbiota, we followed mice from <i>M. tuberculosis</i> aerosol infection until death, using 16S rRNA sequencing. We saw a rapid change in the gut microbiota in response to infection, with all mice showing a loss and then recovery of microbial community diversity, and found that pre-infection samples clustered separately from post-infection samples, using ecological beta-diversity measures. The effect on the fecal microbiota was observed as rapidly as six days following lung infection. Analysis of additional mice infected by a different <i>M. tuberculosis</i> strain corroborated these results, together demonstrating that the mouse gut microbiota significantly changes with <i>M. tuberculosis</i> infection.</p></div

Phylogenetic profile of bacterial genera for uninfected and <i>M. tuberculosis</i> H37Rv infected mice.

<p>Stacked bar charts for uninfected and H37Rv-infected mice of the 16 main genera identified based on ≥1% abundance present in at least two samples. Unclassified sequences are not shown. The black colored bar along x-axis indicates the five uninfected mice, while the red colored bar indicates mice infected with H37Rv.</p

Differentially abundant OTUs identified between uninfected and <i>M. tuberculosis</i> H37Rv infected mice.

<p>OTUs are ordered by consensus taxonomic classification, with OTUs scaled by relative abundances for each row ranging from low relative abundance (blue) to high relative abundance (red).</p

Differentially abundant OTUs identified between pre-infection and post-infection.

<p>OTUs are ordered by consensus taxonomic classification, with OTUs scaled by relative abundances for each row ranging from low relative abundance (blue) to high relative abundance (red).</p